Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning
Descripción del Articulo
Nowadays, implementing data analytics is necessary to improve the collection, evaluation, analysis, and organization of data that allow the discovery of patterns, correlations, and trends that improve knowledge management, development of strategies, and decision-making in the organization. Therefore...
Autores: | , , , , , , , , , |
---|---|
Formato: | artículo |
Fecha de Publicación: | 2024 |
Institución: | Universidad Peruana de Ciencias Aplicadas |
Repositorio: | UPC-Institucional |
Lenguaje: | inglés |
OAI Identifier: | oai:repositorioacademico.upc.edu.pe:10757/676071 |
Enlace del recurso: | http://hdl.handle.net/10757/676071 |
Nivel de acceso: | acceso embargado |
Materia: | Data analytics Data extraction knowledge creation knowledge management machine learning predictive analytics storage of knowledge |
id |
UUPC_93c63e739f9cec6148dba4c8acbabf79 |
---|---|
oai_identifier_str |
oai:repositorioacademico.upc.edu.pe:10757/676071 |
network_acronym_str |
UUPC |
network_name_str |
UPC-Institucional |
repository_id_str |
2670 |
dc.title.es_PE.fl_str_mv |
Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning |
title |
Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning |
spellingShingle |
Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning Pariona-Luque, Rosario Data analytics Data extraction knowledge creation knowledge management machine learning predictive analytics storage of knowledge |
title_short |
Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning |
title_full |
Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning |
title_fullStr |
Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning |
title_full_unstemmed |
Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning |
title_sort |
Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning |
author |
Pariona-Luque, Rosario |
author_facet |
Pariona-Luque, Rosario Pacheco, Alex Vegas-Gallo, Edwin Castanho, Rui Alexandre Lema, Fabian Pacheco-Pumaleque, Liz Añaños-Bedriñana, Marco Marin, Wilson Felix-Poicon, Edwin Loures, Ana |
author_role |
author |
author2 |
Pacheco, Alex Vegas-Gallo, Edwin Castanho, Rui Alexandre Lema, Fabian Pacheco-Pumaleque, Liz Añaños-Bedriñana, Marco Marin, Wilson Felix-Poicon, Edwin Loures, Ana |
author2_role |
author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Pariona-Luque, Rosario Pacheco, Alex Vegas-Gallo, Edwin Castanho, Rui Alexandre Lema, Fabian Pacheco-Pumaleque, Liz Añaños-Bedriñana, Marco Marin, Wilson Felix-Poicon, Edwin Loures, Ana |
dc.subject.es_PE.fl_str_mv |
Data analytics Data extraction knowledge creation knowledge management machine learning predictive analytics storage of knowledge |
topic |
Data analytics Data extraction knowledge creation knowledge management machine learning predictive analytics storage of knowledge |
description |
Nowadays, implementing data analytics is necessary to improve the collection, evaluation, analysis, and organization of data that allow the discovery of patterns, correlations, and trends that improve knowledge management, development of strategies, and decision-making in the organization. Therefore, this study aims to provide an accurate and detailed assessment of the current state of data analytics in the retail sector, identifying specific areas of improvement to strengthen knowledge management in organizations. The research is applied with a quantitative approach and non-experimental design at a descriptive and propositional level. The survey technique was used, and as a data collection instrument, a questionnaire addressed to 351 employees of companies in the retail sector concerning the variable data analysis with the dimensions of data extraction, predictive analysis, and machine learning and the variable management of the knowledge with the dimensions knowledge creation and knowledge storage. The results show that 52.99% of collaborators indicate that the level of data extraction is terrible, 57.83% indicate that the level of predictive analysis is wrong, and 54.99% express that the level of machine learning is average, which contributes to the implementation of innovative resources and solutions that promote the inclusion of a high-tech approach to address information management problems and contribution to the development of knowledge in an institution. |
publishDate |
2024 |
dc.date.accessioned.none.fl_str_mv |
2024-10-10T06:59:28Z |
dc.date.available.none.fl_str_mv |
2024-10-10T06:59:28Z |
dc.date.issued.fl_str_mv |
2024-01-01 |
dc.type.es_PE.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
dc.identifier.issn.none.fl_str_mv |
11099526 |
dc.identifier.doi.none.fl_str_mv |
10.37394/23207.2024.21.126 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10757/676071 |
dc.identifier.eissn.none.fl_str_mv |
22242899 |
dc.identifier.journal.es_PE.fl_str_mv |
WSEAS Transactions on Business and Economics |
dc.identifier.eid.none.fl_str_mv |
2-s2.0-85202906900 |
dc.identifier.scopusid.none.fl_str_mv |
SCOPUS_ID:85202906900 |
identifier_str_mv |
11099526 10.37394/23207.2024.21.126 22242899 WSEAS Transactions on Business and Economics 2-s2.0-85202906900 SCOPUS_ID:85202906900 |
url |
http://hdl.handle.net/10757/676071 |
dc.language.iso.es_PE.fl_str_mv |
eng |
language |
eng |
dc.rights.es_PE.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.es_PE.fl_str_mv |
application/html |
dc.publisher.es_PE.fl_str_mv |
World Scientific and Engineering Academy and Society |
dc.source.none.fl_str_mv |
reponame:UPC-Institucional instname:Universidad Peruana de Ciencias Aplicadas instacron:UPC |
instname_str |
Universidad Peruana de Ciencias Aplicadas |
instacron_str |
UPC |
institution |
UPC |
reponame_str |
UPC-Institucional |
collection |
UPC-Institucional |
dc.source.journaltitle.none.fl_str_mv |
WSEAS Transactions on Business and Economics |
dc.source.volume.none.fl_str_mv |
21 |
dc.source.beginpage.none.fl_str_mv |
1546 |
dc.source.endpage.none.fl_str_mv |
1556 |
bitstream.url.fl_str_mv |
https://repositorioacademico.upc.edu.pe/bitstream/10757/676071/1/license.txt |
bitstream.checksum.fl_str_mv |
8a4605be74aa9ea9d79846c1fba20a33 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 |
repository.name.fl_str_mv |
Repositorio académico upc |
repository.mail.fl_str_mv |
upc@openrepository.com |
_version_ |
1837187181942145024 |
spelling |
61951bd049a2235613f60a00f14dee4b3006fb68e4759a60b4ff00ecf2734abdca5c4f35317622b16c89831959e9774b0ec3004b0455e621fbe6f85e7669adda829098300d2bc9a9592c0e05fb8b5f94fa71e34d53002b7deefe07ec285247d1a716725d79b0300837f0b798f0e3568b76afe23e45cb93a3009c441d869693964b4fc66640826362593003ee8640dd1697104bf1a30e7288e2c86300f3dfb1b55412fecc31cc02e4110e6ea3300Pariona-Luque, RosarioPacheco, AlexVegas-Gallo, EdwinCastanho, Rui AlexandreLema, FabianPacheco-Pumaleque, LizAñaños-Bedriñana, MarcoMarin, WilsonFelix-Poicon, EdwinLoures, Ana2024-10-10T06:59:28Z2024-10-10T06:59:28Z2024-01-011109952610.37394/23207.2024.21.126http://hdl.handle.net/10757/67607122242899WSEAS Transactions on Business and Economics2-s2.0-85202906900SCOPUS_ID:85202906900Nowadays, implementing data analytics is necessary to improve the collection, evaluation, analysis, and organization of data that allow the discovery of patterns, correlations, and trends that improve knowledge management, development of strategies, and decision-making in the organization. Therefore, this study aims to provide an accurate and detailed assessment of the current state of data analytics in the retail sector, identifying specific areas of improvement to strengthen knowledge management in organizations. The research is applied with a quantitative approach and non-experimental design at a descriptive and propositional level. The survey technique was used, and as a data collection instrument, a questionnaire addressed to 351 employees of companies in the retail sector concerning the variable data analysis with the dimensions of data extraction, predictive analysis, and machine learning and the variable management of the knowledge with the dimensions knowledge creation and knowledge storage. The results show that 52.99% of collaborators indicate that the level of data extraction is terrible, 57.83% indicate that the level of predictive analysis is wrong, and 54.99% express that the level of machine learning is average, which contributes to the implementation of innovative resources and solutions that promote the inclusion of a high-tech approach to address information management problems and contribution to the development of knowledge in an institution.Fundação para a Ciência e a Tecnologiaapplication/htmlengWorld Scientific and Engineering Academy and Societyinfo:eu-repo/semantics/embargoedAccessData analyticsData extractionknowledge creationknowledge managementmachine learningpredictive analyticsstorage of knowledgeAssessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learninginfo:eu-repo/semantics/articleWSEAS Transactions on Business and Economics2115461556reponame:UPC-Institucionalinstname:Universidad Peruana de Ciencias Aplicadasinstacron:UPCLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorioacademico.upc.edu.pe/bitstream/10757/676071/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51false10757/676071oai:repositorioacademico.upc.edu.pe:10757/6760712024-10-10 06:59:30.252Repositorio académico upcupc@openrepository.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 |
score |
13.95948 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).